code: def test_lpca_random_noise(rng):
      S0 = 100 + 2 * rng.standard_normal((22, 23, 30, 20))
      S0ns = localpca(S0, sigma=np.std(S0))
  
      assert_(S0ns.min() > S0.min())
      assert_(S0ns.max() < S0.max())
      assert_equal(np.round(S0ns.mean()), 100)
  
imports: import warnings
  import numpy
  import scipy.special
  from numpy.testing import assert_, assert_equal, assert_raises, assert_array_almost_equal, assert_warns
  from dipy.denoise.localpca import dimensionality_problem_message, create_patch_radius_arr, compute_patch_size, compute_num_samples, compute_suggested_patch_radius, localpca, mppca, genpca, _pca_classifier
  from dipy.sims.voxel import multi_tensor
  from dipy.core.gradients import gradient_table, generate_bvecs
  from dipy.testing.decorators import set_random_number_generator
label_desc: Use image node_id 4 for calling a global function with example usage: test_lpca_random_noise(rng) without return types